An Inflection Point in Cancer Protein Biomarkers: What was and What's Next

• Published in: Molecular & cellular proteomics 2023-07, Vol.22 (7), p.100569, Article 100569
• Main Authors: Barker, Anna D., Alba, Mario M., Mallick, Parag, Agus, David B., Lee, Jerry S.H.
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Biomarkers - analysis; Biomarkers, Tumor; cancer biomarkers; clinical proteomics; complex adaptive systems; Humans; Neoplasms; protein biomarkers; proteomics
• ISSN: 1535-9476; 1535-9484; 1535-9484
• DOI: 10.1016/j.mcpro.2023.100569

Biomarkers remain the highest value proposition in cancer medicine today—especially protein biomarkers. Despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system, and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last 2 decades have seen an explosion of multiomics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single-cell analysis, artificial intelligence (machine and deep learning) for data analysis, and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking toward viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to redefine biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be “game changing” for the clinical outcome of cancer patients. [Display omitted] •Protein biomarkers will be key to deconvoluting the complexity of cancer.•Redefine biomarkers as representations of hierarchical biological system states.•Big data and AI could be transformative for biomarker discovery in the future.•Biomarkers discovery will require theoretical constructs to separate signal from noise.•Build mechanistic frameworks to enable new data analytics in context of prior studies. Biomarker